Overview

Dataset statistics

Number of variables9
Number of observations239808
Missing cells481472
Missing cells (%)22.3%
Duplicate rows13224
Duplicate rows (%)5.5%
Total size in memory18.3 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 13224 (5.5%) duplicate rowsDuplicates
wime_komfort is highly overall correlated with wime_sauberkeit and 2 other fieldsHigh correlation
wime_sauberkeit is highly overall correlated with wime_komfort and 1 other fieldsHigh correlation
wime_platzangebot is highly overall correlated with wime_komfort and 2 other fieldsHigh correlation
wime_gesamtzuf is highly overall correlated with wime_komfort and 1 other fieldsHigh correlation
wime_personal has 156757 (65.4%) missing valuesMissing
wime_komfort has 52988 (22.1%) missing valuesMissing
wime_sauberkeit has 50007 (20.9%) missing valuesMissing
wime_puenktlich has 49370 (20.6%) missing valuesMissing
wime_platzangebot has 48545 (20.2%) missing valuesMissing
wime_gesamtzuf has 41311 (17.2%) missing valuesMissing
wime_preis_leistung has 15977 (6.7%) missing valuesMissing
wime_fahrplan has 8742 (3.6%) missing valuesMissing
wime_oes_fahrt has 57775 (24.1%) missing valuesMissing
wime_komfort has 3037 (1.3%) zerosZeros
wime_puenktlich has 4923 (2.1%) zerosZeros
wime_platzangebot has 6690 (2.8%) zerosZeros
wime_preis_leistung has 6776 (2.8%) zerosZeros
wime_fahrplan has 5635 (2.3%) zerosZeros

Reproduction

Analysis started2023-01-04 11:25:53.626929
Analysis finished2023-01-04 11:26:06.741339
Duration13.11 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

wime_personal
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing156757
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean89.978648
Minimum0
Maximum100
Zeros721
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:06.809066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q177.777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.222222

Descriptive statistics

Standard deviation17.720196
Coefficient of variation (CV)0.1969378
Kurtosis6.7759742
Mean89.978648
Median Absolute Deviation (MAD)0
Skewness-2.3642722
Sum7472816.7
Variance314.00536
MonotonicityNot monotonic
2023-01-04T12:26:06.910197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 54872
 
22.9%
75 10658
 
4.4%
88.88888889 5367
 
2.2%
77.77777778 4839
 
2.0%
50 1918
 
0.8%
66.66666667 1877
 
0.8%
44.44444444 885
 
0.4%
55.55555556 817
 
0.3%
0 721
 
0.3%
25 468
 
0.2%
Other values (3) 629
 
0.3%
(Missing) 156757
65.4%
ValueCountFrequency (%)
0 721
 
0.3%
11.11111111 138
 
0.1%
22.22222222 227
 
0.1%
25 468
 
0.2%
33.33333333 264
 
0.1%
44.44444444 885
 
0.4%
50 1918
 
0.8%
55.55555556 817
 
0.3%
66.66666667 1877
 
0.8%
75 10658
4.4%
ValueCountFrequency (%)
100 54872
22.9%
88.88888889 5367
 
2.2%
77.77777778 4839
 
2.0%
75 10658
 
4.4%
66.66666667 1877
 
0.8%
55.55555556 817
 
0.3%
50 1918
 
0.8%
44.44444444 885
 
0.4%
33.33333333 264
 
0.1%
25 468
 
0.2%

wime_komfort
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing52988
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean78.994204
Minimum0
Maximum100
Zeros3037
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:07.013590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.333333
Q175
median77.777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.749961
Coefficient of variation (CV)0.28799532
Kurtosis1.4874209
Mean78.994204
Median Absolute Deviation (MAD)22.222222
Skewness-1.2399088
Sum14757697
Variance517.56072
MonotonicityNot monotonic
2023-01-04T12:26:07.117295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 71717
29.9%
75 40918
17.1%
77.77777778 17438
 
7.3%
88.88888889 12623
 
5.3%
50 11299
 
4.7%
66.66666667 10817
 
4.5%
55.55555556 5996
 
2.5%
44.44444444 4845
 
2.0%
25 3087
 
1.3%
0 3037
 
1.3%
Other values (3) 5043
 
2.1%
(Missing) 52988
22.1%
ValueCountFrequency (%)
0 3037
 
1.3%
11.11111111 1002
 
0.4%
22.22222222 1685
 
0.7%
25 3087
 
1.3%
33.33333333 2356
 
1.0%
44.44444444 4845
 
2.0%
50 11299
 
4.7%
55.55555556 5996
 
2.5%
66.66666667 10817
 
4.5%
75 40918
17.1%
ValueCountFrequency (%)
100 71717
29.9%
88.88888889 12623
 
5.3%
77.77777778 17438
 
7.3%
75 40918
17.1%
66.66666667 10817
 
4.5%
55.55555556 5996
 
2.5%
50 11299
 
4.7%
44.44444444 4845
 
2.0%
33.33333333 2356
 
1.0%
25 3087
 
1.3%

wime_sauberkeit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing50007
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean79.214165
Minimum0
Maximum100
Zeros1706
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:07.222732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.444444
Q175
median77.777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.482332
Coefficient of variation (CV)0.27119307
Kurtosis1.1260068
Mean79.214165
Median Absolute Deviation (MAD)22.222222
Skewness-1.0950795
Sum15034928
Variance461.4906
MonotonicityNot monotonic
2023-01-04T12:26:07.326055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 70170
29.3%
75 43649
18.2%
77.77777778 18011
 
7.5%
50 14083
 
5.9%
88.88888889 13642
 
5.7%
66.66666667 10775
 
4.5%
55.55555556 5581
 
2.3%
44.44444444 4508
 
1.9%
25 3585
 
1.5%
33.33333333 2167
 
0.9%
Other values (3) 3630
 
1.5%
(Missing) 50007
20.9%
ValueCountFrequency (%)
0 1706
 
0.7%
11.11111111 606
 
0.3%
22.22222222 1318
 
0.5%
25 3585
 
1.5%
33.33333333 2167
 
0.9%
44.44444444 4508
 
1.9%
50 14083
 
5.9%
55.55555556 5581
 
2.3%
66.66666667 10775
 
4.5%
75 43649
18.2%
ValueCountFrequency (%)
100 70170
29.3%
88.88888889 13642
 
5.7%
77.77777778 18011
 
7.5%
75 43649
18.2%
66.66666667 10775
 
4.5%
55.55555556 5581
 
2.3%
50 14083
 
5.9%
44.44444444 4508
 
1.9%
33.33333333 2167
 
0.9%
25 3585
 
1.5%

wime_puenktlich
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing49370
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean88.855209
Minimum0
Maximum100
Zeros4923
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:07.433174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.333333
Q188.888889
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11.111111

Descriptive statistics

Standard deviation22.193637
Coefficient of variation (CV)0.24977306
Kurtosis6.0891528
Mean88.855209
Median Absolute Deviation (MAD)0
Skewness-2.4968692
Sum16921408
Variance492.55754
MonotonicityNot monotonic
2023-01-04T12:26:07.535166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 131705
54.9%
75 19226
 
8.0%
88.88888889 11702
 
4.9%
77.77777778 7321
 
3.1%
0 4923
 
2.1%
50 4589
 
1.9%
66.66666667 2952
 
1.2%
25 2359
 
1.0%
44.44444444 1566
 
0.7%
55.55555556 1485
 
0.6%
Other values (3) 2610
 
1.1%
(Missing) 49370
 
20.6%
ValueCountFrequency (%)
0 4923
 
2.1%
11.11111111 629
 
0.3%
22.22222222 988
 
0.4%
25 2359
 
1.0%
33.33333333 993
 
0.4%
44.44444444 1566
 
0.7%
50 4589
 
1.9%
55.55555556 1485
 
0.6%
66.66666667 2952
 
1.2%
75 19226
8.0%
ValueCountFrequency (%)
100 131705
54.9%
88.88888889 11702
 
4.9%
77.77777778 7321
 
3.1%
75 19226
 
8.0%
66.66666667 2952
 
1.2%
55.55555556 1485
 
0.6%
50 4589
 
1.9%
44.44444444 1566
 
0.7%
33.33333333 993
 
0.4%
25 2359
 
1.0%

wime_platzangebot
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing48545
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean80.114264
Minimum0
Maximum100
Zeros6690
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:07.640297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.222222
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation26.782415
Coefficient of variation (CV)0.33430271
Kurtosis1.3453852
Mean80.114264
Median Absolute Deviation (MAD)0
Skewness-1.4406137
Sum15322894
Variance717.29776
MonotonicityNot monotonic
2023-01-04T12:26:08.685526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 96371
40.2%
75 28605
 
11.9%
77.77777778 12081
 
5.0%
50 10865
 
4.5%
88.88888889 10341
 
4.3%
66.66666667 6876
 
2.9%
0 6690
 
2.8%
25 5097
 
2.1%
55.55555556 4096
 
1.7%
44.44444444 3937
 
1.6%
Other values (3) 6304
 
2.6%
(Missing) 48545
20.2%
ValueCountFrequency (%)
0 6690
 
2.8%
11.11111111 1549
 
0.6%
22.22222222 2316
 
1.0%
25 5097
 
2.1%
33.33333333 2439
 
1.0%
44.44444444 3937
 
1.6%
50 10865
 
4.5%
55.55555556 4096
 
1.7%
66.66666667 6876
 
2.9%
75 28605
11.9%
ValueCountFrequency (%)
100 96371
40.2%
88.88888889 10341
 
4.3%
77.77777778 12081
 
5.0%
75 28605
 
11.9%
66.66666667 6876
 
2.9%
55.55555556 4096
 
1.7%
50 10865
 
4.5%
44.44444444 3937
 
1.6%
33.33333333 2439
 
1.0%
25 5097
 
2.1%

wime_gesamtzuf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing41311
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean84.600425
Minimum0
Maximum100
Zeros2142
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:08.791196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q175
median88.888889
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.563135
Coefficient of variation (CV)0.23124157
Kurtosis3.7255098
Mean84.600425
Median Absolute Deviation (MAD)11.111111
Skewness-1.7166711
Sum16792931
Variance382.71624
MonotonicityNot monotonic
2023-01-04T12:26:08.889579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 93325
38.9%
75 41862
17.5%
88.88888889 20359
 
8.5%
77.77777778 16801
 
7.0%
50 7771
 
3.2%
66.66666667 6768
 
2.8%
55.55555556 2801
 
1.2%
44.44444444 2196
 
0.9%
0 2142
 
0.9%
25 2035
 
0.8%
Other values (3) 2437
 
1.0%
(Missing) 41311
17.2%
ValueCountFrequency (%)
0 2142
 
0.9%
11.11111111 475
 
0.2%
22.22222222 915
 
0.4%
25 2035
 
0.8%
33.33333333 1047
 
0.4%
44.44444444 2196
 
0.9%
50 7771
 
3.2%
55.55555556 2801
 
1.2%
66.66666667 6768
 
2.8%
75 41862
17.5%
ValueCountFrequency (%)
100 93325
38.9%
88.88888889 20359
 
8.5%
77.77777778 16801
 
7.0%
75 41862
17.5%
66.66666667 6768
 
2.8%
55.55555556 2801
 
1.2%
50 7771
 
3.2%
44.44444444 2196
 
0.9%
33.33333333 1047
 
0.4%
25 2035
 
0.8%

wime_preis_leistung
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing15977
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean73.835878
Minimum0
Maximum100
Zeros6776
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:08.992251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q150
median75
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation26.383204
Coefficient of variation (CV)0.35732227
Kurtosis0.23452505
Mean73.835878
Median Absolute Deviation (MAD)25
Skewness-0.91730396
Sum16526758
Variance696.07343
MonotonicityNot monotonic
2023-01-04T12:26:09.091329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 79704
33.2%
75 49834
20.8%
50 27836
 
11.6%
77.77777778 13293
 
5.5%
66.66666667 10076
 
4.2%
25 9103
 
3.8%
88.88888889 7804
 
3.3%
0 6776
 
2.8%
55.55555556 6284
 
2.6%
44.44444444 6253
 
2.6%
Other values (3) 6868
 
2.9%
(Missing) 15977
 
6.7%
ValueCountFrequency (%)
0 6776
 
2.8%
11.11111111 1219
 
0.5%
22.22222222 2588
 
1.1%
25 9103
 
3.8%
33.33333333 3061
 
1.3%
44.44444444 6253
 
2.6%
50 27836
11.6%
55.55555556 6284
 
2.6%
66.66666667 10076
 
4.2%
75 49834
20.8%
ValueCountFrequency (%)
100 79704
33.2%
88.88888889 7804
 
3.3%
77.77777778 13293
 
5.5%
75 49834
20.8%
66.66666667 10076
 
4.2%
55.55555556 6284
 
2.6%
50 27836
 
11.6%
44.44444444 6253
 
2.6%
33.33333333 3061
 
1.3%
25 9103
 
3.8%

wime_fahrplan
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing8742
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.522288
Minimum0
Maximum100
Zeros5635
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:09.192721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.91105
Coefficient of variation (CV)0.28628346
Kurtosis2.5876559
Mean83.522288
Median Absolute Deviation (MAD)0
Skewness-1.7013307
Sum19299161
Variance571.7383
MonotonicityNot monotonic
2023-01-04T12:26:09.293998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 126675
52.8%
75 35519
 
14.8%
77.77777778 14678
 
6.1%
88.88888889 12666
 
5.3%
50 11453
 
4.8%
66.66666667 7748
 
3.2%
0 5635
 
2.3%
25 4731
 
2.0%
55.55555556 3956
 
1.6%
44.44444444 3745
 
1.6%
Other values (3) 4260
 
1.8%
(Missing) 8742
 
3.6%
ValueCountFrequency (%)
0 5635
 
2.3%
11.11111111 840
 
0.4%
22.22222222 1509
 
0.6%
25 4731
 
2.0%
33.33333333 1911
 
0.8%
44.44444444 3745
 
1.6%
50 11453
 
4.8%
55.55555556 3956
 
1.6%
66.66666667 7748
 
3.2%
75 35519
14.8%
ValueCountFrequency (%)
100 126675
52.8%
88.88888889 12666
 
5.3%
77.77777778 14678
 
6.1%
75 35519
 
14.8%
66.66666667 7748
 
3.2%
55.55555556 3956
 
1.6%
50 11453
 
4.8%
44.44444444 3745
 
1.6%
33.33333333 1911
 
0.8%
25 4731
 
2.0%

wime_oes_fahrt
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing57775
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean90.707897
Minimum0
Maximum100
Zeros423
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-04T12:26:09.397012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.666667
Q177.777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.222222

Descriptive statistics

Standard deviation14.743394
Coefficient of variation (CV)0.16253705
Kurtosis5.5228798
Mean90.707897
Median Absolute Deviation (MAD)0
Skewness-2.0036761
Sum16511831
Variance217.36765
MonotonicityNot monotonic
2023-01-04T12:26:09.499139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 114581
47.8%
75 25739
 
10.7%
88.88888889 16983
 
7.1%
77.77777778 12813
 
5.3%
66.66666667 4225
 
1.8%
50 3579
 
1.5%
55.55555556 1485
 
0.6%
44.44444444 943
 
0.4%
25 632
 
0.3%
0 423
 
0.2%
Other values (3) 630
 
0.3%
(Missing) 57775
24.1%
ValueCountFrequency (%)
0 423
 
0.2%
11.11111111 106
 
< 0.1%
22.22222222 210
 
0.1%
25 632
 
0.3%
33.33333333 314
 
0.1%
44.44444444 943
 
0.4%
50 3579
 
1.5%
55.55555556 1485
 
0.6%
66.66666667 4225
 
1.8%
75 25739
10.7%
ValueCountFrequency (%)
100 114581
47.8%
88.88888889 16983
 
7.1%
77.77777778 12813
 
5.3%
75 25739
 
10.7%
66.66666667 4225
 
1.8%
55.55555556 1485
 
0.6%
50 3579
 
1.5%
44.44444444 943
 
0.4%
33.33333333 314
 
0.1%
25 632
 
0.3%

Interactions

2023-01-04T12:26:04.281925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.009115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.122822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.262359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.421366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.572557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.736767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.906753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.084680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.412605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.155286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.250386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.392987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.551174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.704553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.869154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.038532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.219911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.543328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.273941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.376335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.523679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.682227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.836885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.001758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.170477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.353618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.675434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.400784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.503126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.652845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.809674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.969098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.133884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.301300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.488124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.806316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.536677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.634450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.782389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.937794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.100282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.266261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.433920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.623557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.936358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.654030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.760172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.914151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.065206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.228614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.397948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.568615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.761081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:05.061495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.770456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.887003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.041314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.193747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.356121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.526876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.702943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:03.899923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:05.191803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:55.889867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.015318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.172322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.323980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.487041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.657734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.835654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.035894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:05.319959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:56.005411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:57.142271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:58.299290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:25:59.451010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:00.615463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:01.784973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:02.961240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T12:26:04.162629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-04T12:26:09.600768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
wime_personal1.0000.4310.4120.4160.4400.4710.3500.4130.359
wime_komfort0.4311.0000.6310.3630.5490.5520.4010.3930.355
wime_sauberkeit0.4120.6311.0000.3340.5270.5000.3500.3350.373
wime_puenktlich0.4160.3630.3341.0000.3700.4570.2940.4210.305
wime_platzangebot0.4400.5490.5270.3701.0000.5180.3780.3620.312
wime_gesamtzuf0.4710.5520.5000.4570.5181.0000.4980.4900.430
wime_preis_leistung0.3500.4010.3500.2940.3780.4981.0000.4380.259
wime_fahrplan0.4130.3930.3350.4210.3620.4900.4381.0000.293
wime_oes_fahrt0.3590.3550.3730.3050.3120.4300.2590.2931.000

Missing values

2023-01-04T12:26:05.475414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-04T12:26:05.775407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-04T12:26:06.492747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
239930NaNNaNNaNNaNNaNNaNNaNNaNNaN
239915100.025.075.0100.0100.0100.075.0100.075.0
239912NaN100.0100.0100.0100.0100.025.0100.075.0
239911NaN75.0100.0100.0100.0100.075.075.0100.0
239908NaNNaNNaNNaNNaNNaNNaNNaNNaN
239901NaNNaNNaNNaNNaNNaN50.0100.0NaN
239938NaNNaNNaNNaNNaNNaNNaNNaNNaN
239919NaN100.0100.0100.0100.0100.0100.0100.0100.0
239956100.0100.075.0100.0100.0100.0100.0100.0100.0
239894NaNNaNNaNNaNNaNNaN50.050.0NaN
wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
880100.077.77777855.555556100.000000100.00000077.77777877.777778100.000000100.000000
881NaN100.00000077.777778100.00000077.77777888.88888933.33333344.444444100.000000
882NaN77.77777866.666667100.00000066.66666777.77777888.88888977.77777877.777778
883100.088.88888977.777778100.00000088.88888988.88888988.88888988.88888988.888889
884NaN66.666667100.000000100.000000100.00000088.88888966.666667100.000000100.000000
885NaN100.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000
886100.022.22222255.55555611.1111110.00000033.3333330.00000022.22222244.444444
887NaN77.77777855.555556100.000000100.00000077.7777780.000000100.000000100.000000
888NaN66.66666777.777778100.00000066.66666777.77777855.55555655.55555666.666667
913NaN100.000000100.00000088.888889100.00000088.888889100.000000100.000000100.000000

Duplicate rows

Most frequently occurring

wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt# duplicates
12573NaN100.0100.0100.0100.0100.0100.0100.0100.011604
13212NaNNaNNaNNaNNaNNaN100.0100.0NaN10646
5136100.0100.0100.0100.0100.0100.0100.0100.0100.08966
13223NaNNaNNaNNaNNaNNaNNaNNaNNaN7240
13178NaNNaNNaNNaNNaNNaN75.0100.0NaN5144
13177NaNNaNNaNNaNNaNNaN75.075.0NaN3451
13150NaNNaNNaNNaNNaNNaN50.0100.0NaN2516
13089NaNNaNNaNNaNNaN100.0100.0100.0NaN2268
12501NaN100.0100.0100.0100.0100.075.0100.0100.02105
5077100.0100.0100.0100.0100.0100.075.0100.0100.02047